Research on spatial prediction technology for mitigating tunnel inrush disasters under complex geological conditions in China’s Hengduan Mountain Range by Yang Zou, XiuJun Dong, Tao Feng, ZhengXuan Xu, Hailin He, ZhangLei Wu

“…This spatial prediction and analysis method is highly effective and has practical and promotional value.…”
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Identifying species traits that predict vulnerability to climate change by Damien A. Fordham

“…A powerful solution is to analyse the growing volume of biological data on changes in species ranges and abundances using process-explicit ecological models that run at fine temporal and spatial scales and across large geographical extents. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. …”
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Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System by Jacob A. Macdonald, David M. Barnard, Kyle R. Mankin, Grace L. Miner, Robert H. Erskine, David J. Poss, Sushant Mehan, Adam L. Mahood, Maysoon M. Mikha

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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. …”
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Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning by Alireza Mohammadi, Bardia Mashhoodi, Ali Shamsoddini, Elahe Pishgar, Robert Bergquist

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BUILDING PREDICTIVE MODELS TO ASSESS DEGRADATION OF SOIL ORGANIC MATTER OVER TIME USING REMOTE SENSING DATA by Abdulsalam Aljumaily, Ammar Kashmolaa

“…The results of the study showed the possibility of applying predictive models to Satellite data for a particular area and for previous years to give results with high spatial accuracy (R2 = 0.9581). …”
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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, <i>p</i> < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. …”
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The spatial structure of stimuli shapes the timescale of correlations in population spiking activity. by Ashok Litwin-Kumar, Maurice J Chacron, Brent Doiron

“…Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. …”
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Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles. by Alexandra V Kulinkina, Yvonne Walz, Magaly Koch, Nana-Kwadwo Biritwum, Jürg Utzinger, Elena N Naumova

“…We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.<h4>Methodology</h4>Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). …”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…The sampling strategy was designed to ensure comprehensive data collection, allowing for robust model training and validation. MaxEnt, which is a presence-only model, outperformed both the GARP and logistic regression models in predicting suitable habitats for Astragalus sp. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. …”
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EMGP-Net: A Hybrid Deep Learning Architecture for Breast Cancer Gene Expression Prediction by Oumeima Thâalbi, Moulay A. Akhloufi

“…Recent studies have used whole-slide images combined with spatial transcriptomics data to predict breast cancer gene expression. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…First, the time series of data stream used for prediction is subjected to two-stage weighting processing,and then the processed time series and its dependent spatial topology information are input into the network model for spatiotemporal feature extraction. …”
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Multidimensional assessment of the spatiotemporal evolution, driving mechanisms, and future predictions of urban heat islands in Jinan, China by Lingye Tan, Tiong Lee Kong Robert, Yan Zhang, Siyi Huang, Ziyang Zhang

“…By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. …”
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A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model by Lei HAN, Xinxin QI, Tianjun WANG, Chunmin PEI, Xudong WANG

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High-resolution spatial prediction of anemia risk among children aged 6 to 59 months in low- and middle-income countries by Johannes Seiler, Mattias Wetscher, Kenneth Harttgen, Jürg Utzinger, Nikolaus Umlauf

“…Methods Employing full probabilistic Bayesian distributional regression models, the research accurately predicts age-specific and spatially varying anemia risks. …”
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FibroRegNet: A Regression Framework for the Pulmonary Fibrosis Prognosis Prediction Using a Convolutional Spatial Transformer Network by Pardhasaradhi Mittapalli, V. Thanikaiselvan

“…Predicting the growth of idiopathic pulmonary fibrosis (IPF) is crucial for effectively treating patients affected by the disease. …”
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Characteristics of Spatial and Temporal Evolution of Coastal Wetland Landscape Patterns and Prediction Analysis—A Case Study of Panjin Wetland, China by Qian Cheng, Ruixin Chen, Wei Xu, Meiqing Wang

“…For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. …”
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A Review of Wind Power Prediction Methods Based on Multi-Time Scales by Fan Li, Hongzhen Wang, Dan Wang, Dong Liu, Ke Sun

“…Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. …”
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